import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


_SCREAMING_SNAKE_CASE 		=      ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
_SCREAMING_SNAKE_CASE 		=      {"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
     raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      """ Hello world! cécé herlolip"""

_SCREAMING_SNAKE_CASE 		=      [
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]





def 			lowercase(	UpperCamelCase_       )		->	List[str]:







 '''simple docstring'''
 UpperCamelCase			  =      [
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """_float_tensor""",
 ]
 for k in ignore_keys:
  state_dict.pop(UpperCamelCase_							,					UpperCamelCase_       )





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      dct.pop(UpperCamelCase_       )
 UpperCamelCase			  =      val





def 			lowercase(	UpperCamelCase_       )		->	Optional[int]:







 '''simple docstring'''
 UpperCamelCase			  =      torch.load(UpperCamelCase_							,					map_location="""cpu"""       )
 UpperCamelCase			  =      torch.hub.load("""pytorch/fairseq"""							,					"""bart.large.cnn"""       ).eval()
 hub_interface.model.load_state_dict(sd["""model"""]       )
 return hub_interface





def 			lowercase(	UpperCamelCase_       )		->	Dict:







 '''simple docstring'''
 UpperCamelCase  ,     UpperCamelCase			  =      emb.weight.shape
 UpperCamelCase			  =      nn.Linear(UpperCamelCase_							,					UpperCamelCase_							,					bias=UpperCamelCase_       )
 UpperCamelCase			  =      emb.weight.data
 return lin_layer





@torch.no_grad()
def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_=None       )		->	int:







 '''simple docstring'''
 if not os.path.exists(UpperCamelCase_       ):
  UpperCamelCase			  =      torch.hub.load("""pytorch/fairseq"""							,					UpperCamelCase_       ).eval()
 else:
  UpperCamelCase			  =      load_xsum_checkpoint(UpperCamelCase_       )

 bart.model.upgrade_state_dict(bart.model.state_dict()       )
 if hf_checkpoint_name is None:
  UpperCamelCase			  =      checkpoint_path.replace("""."""							,					"""-"""       )
 UpperCamelCase			  =      BartConfig.from_pretrained(UpperCamelCase_       )
 UpperCamelCase			  =      bart.encode(UpperCamelCase_       ).unsqueeze(0       )
 UpperCamelCase			  =      BartTokenizer.from_pretrained(UpperCamelCase_       ).encode(UpperCamelCase_							,					return_tensors="""pt"""       ).unsqueeze(0       )
 if not torch.eq(UpperCamelCase_							,					UpperCamelCase_       ).all():
  raise ValueError(
      f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

 if checkpoint_path == "bart.large.mnli":
  UpperCamelCase			  =      bart.state_dict()
  remove_ignore_keys_(UpperCamelCase_       )
  UpperCamelCase			  =      state_dict["""model.decoder.embed_tokens.weight"""]
  for src, dest in mnli_rename_keys:
   rename_key(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
  UpperCamelCase			  =      BartForSequenceClassification(UpperCamelCase_       ).eval()
  model.load_state_dict(UpperCamelCase_       )
  UpperCamelCase			  =      bart.predict("""mnli"""							,					UpperCamelCase_							,					return_logits=UpperCamelCase_       )
  UpperCamelCase			  =      model(UpperCamelCase_       )[0]  # logits
 else:  # no classification heads to worry about
  UpperCamelCase			  =      bart.model.state_dict()
  remove_ignore_keys_(UpperCamelCase_       )
  UpperCamelCase			  =      state_dict["""decoder.embed_tokens.weight"""]
  UpperCamelCase			  =      bart.extract_features(UpperCamelCase_       )
  if hf_checkpoint_name == "facebook/bart-large":
   UpperCamelCase			  =      BartModel(UpperCamelCase_       ).eval()
   model.load_state_dict(UpperCamelCase_       )
   UpperCamelCase			  =      model(UpperCamelCase_       ).model[0]
  else:
   UpperCamelCase			  =      BartForConditionalGeneration(UpperCamelCase_       ).eval()  # an existing summarization ckpt
   model.model.load_state_dict(UpperCamelCase_       )
   if hasattr(UpperCamelCase_							,					"""lm_head"""       ):
    UpperCamelCase			  =      make_linear_from_emb(model.model.shared       )
   UpperCamelCase			  =      model.model(UpperCamelCase_       )[0]

    # Check results
 if fairseq_output.shape != new_model_outputs.shape:
  raise ValueError(
      f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
 if (fairseq_output != new_model_outputs).any().item():
  raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
 Path(UpperCamelCase_       ).mkdir(exist_ok=UpperCamelCase_       )
 model.save_pretrained(UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
     )
     parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument(
         """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)

from datetime import datetime

import requests
from bsa import BeautifulSoup

if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      input("""Enter image url: """).strip()
     print(F'''Downloading image from {url} ...''')
     _SCREAMING_SNAKE_CASE 		=      BeautifulSoup(requests.get(url).content, """html.parser""")
     # The image URL is in the content field of the first meta tag with property og:image
     _SCREAMING_SNAKE_CASE 		=      soup.find("""meta""", {"""property""": """og:image"""})["""content"""]
     _SCREAMING_SNAKE_CASE 		=      requests.get(image_url).content
     _SCREAMING_SNAKE_CASE 		=      F'''{datetime.now():%Y-%m-%d_%H:%M:%S}.jpg'''
     with open(file_name, """wb""") as fp:
          fp.write(image_data)
     print(F'''Done. Image saved to disk as {file_name}.''')
